Security-Aware Task Offloading in IoT Edge Networks Using Software-Defined Networking
Ahmed Raoof Tawfeeq Raoof Tawfeeq Al-Hasani, Ali Broumandnia, Hamid Haj Seyyed Javadi · Mathematical and Computational Applications · 2026
The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately 125±5 ms, a task completion ratio (TCR) of about 92±2%, and a security success rate (SSR) near 95±1.5%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions.